The ability to predict responses of users to items such as goods, services, search results, or other items is useful in many application domains including advertising, product recommendation, information retrieval and others. The response of the user may be his or her mental feeling, view, or opinion of the item. For example, this may be expressed using a rating such as a numerical rating or using words or phrases such as “silly”, “nice”, “cool” or in other ways.
Existing approaches to predicting responses of users to items attempt to find like-minded users and use knowledge of historical responses of those like-minded users to infer responses. For example, product recommendation systems often make recommendations based on knowledge about people who bought the same product x and also bought other different products which can then be recommended. Other approaches attempt to find users who are connected in a social network and use knowledge of historical responses of those users to infer responses. However, these types of systems have limited prediction accuracy.
Existing information retrieval systems often return results which are not relevant or useful and there is an ongoing need to improve. Users often retrieve many results which are not relevant and suffer from an information overload problem.
The embodiments described below are not limited to implementations which solve any or all of the disadvantages of known user response prediction systems.